ArticleJournal of speech, language, and hearing research : JSLHR2023
ParAlg: A Paraphasia Algorithm for Multinomial Classification of Picture Naming Errors.
Article in Journal of speech, language, and hearing research : JSLHR, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Introducing the NEURAL Research Lab Data Set for Studies of Discourse and Gesture in Aphasia and Cognitively Healthy Aging Adults.Journal of speech, language, and hearing research : JSLHR · 2025Article
- Automating Intended Target Identification for Paraphasias in Discourse Using a Large Language Model.Journal of speech, language, and hearing research : JSLHR · 2023Article
- Accuracy of Naming Error Profiles Elicited From Adaptive Short Forms of the Philadelphia Naming Test.Journal of speech, language, and hearing research : JSLHR · 2023Article
- Refining Semantic Similarity of Paraphasias Using a Contextual Language Model.Journal of speech, language, and hearing research : JSLHR · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
purposeA preliminary version of a paraphasia classification algorithm (henceforth called ParAlg) has previously been shown to be a viable method for coding picture naming errors. The purpose of this study is to present an updated version of ParAlg, which uses multinomial classification, and comprehensively evaluate its performance when using two different forms of transcribed input.
methodA subset of 11,999 archival responses produced on the Philadelphia Naming Test were classified into six cardinal paraphasia types using ParAlg under two transcription configurations: (a) using phonemic transcriptions for responses exclusively (
resultsAgreement between ParAlg-generated and human-annotated codes was high, although the
conclusionsParAlg is an accurate and efficient alternative to manual scoring of paraphasias, particularly when lexical responses are orthographically transcribed. With further development, it has the potential to be a useful software application for anomia assessment. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.22087763.
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